Background: Uncertainty in parameters is present in many risk assessment and decision making problems and leads to uncertainty in model predictions. Therefore an analysis of the degree of uncertainty around the model inputs is often needed. Importance analysis involves use of quantitative methods aiming at identifying the contribution of uncertain input model parameters to output uncertainty. Expected value of partial perfect information (EVPPI) measure is a current gold- standard technique for measuring parameters importance in health economics models. The current standard approach of estimating EVPPI through performing double Monte Carlo simulation (MCS) can be associated with a long run time. Objective: To investigate different importance analysis techniques with an aim to find alternative technique with shorter run time that will identify parameters with greatest contribution to uncertainty in model output. Methods: A health economics model was updated and served as a tool to implement various importance analysis techniques. Twelve alternative techniques were applied: rank correlation analysis, contribution to variance analysis, mutual information analysis, dominance analysis, regression analysis, analysis of elasticity, ANCOVA, maximum separation distances analysis, sequential bifurcation, double MCS EVPPI,EVPPI-quadrature and EVPPI- single method. Results: Among all these techniques, the dominance measure resulted with the closest correlated calibrated scores when compared with EVPPI calibrated scores. Performing a dominance analysis as a screening method to identify subgroup of parameters as candidates for being most important parameters and subsequently only performing EVPPI analysis on the selected parameters will reduce the overall run time.

Background: Uncertainty in parameters is present in many risk assessment and decision making problems and leads to uncertainty in model predictions. Therefore an analysis of the degree of uncertainty around the model inputs is often needed. Importance analysis involves use of quantitative methods aiming at identifying the contribution of uncertain input model parameters to output uncertainty. Expected value of partial perfect information (EVPPI) measure is a current gold- standard technique for measuring parameters importance in health economics models. The current standard approach of estimating EVPPI through performing double Monte Carlo simulation (MCS) can be associated with a long run time. Objective: To investigate different importance analysis techniques with an aim to find alternative technique with shorter run time that will identify parameters with greatest contribution to uncertainty in model output. Methods: A health economics model was updated and served as a tool to implement various importance analysis techniques. Twelve alternative techniques were applied: rank correlation analysis, contribution to variance analysis, mutual information analysis, dominance analysis, regression analysis, analysis of elasticity, ANCOVA, maximum separation distances analysis, sequential bifurcation, double MCS EVPPI,EVPPI-quadrature and EVPPI- single method. Results: Among all these techniques, the dominance measure resulted with the closest correlated calibrated scores when compared with EVPPI calibrated scores. Performing a dominance analysis as a screening method to identify subgroup of parameters as candidates for being most important parameters and subsequently only performing EVPPI analysis on the selected parameters will reduce the overall run time.

Background: Uncertainty in parameters is present in many risk assessment and decision making problems and leads to uncertainty in model predictions. Therefore an analysis of the degree of uncertainty around the model inputs is often needed. Importance analysis involves use of quantitative methods aiming at identifying the contribution of uncertain input model parameters to output uncertainty. Expected value of partial perfect information (EVPPI) measure is a current gold- standard technique for measuring parameters importance in health economics models. The current standard approach of estimating EVPPI through performing double Monte Carlo simulation (MCS) can be associated with a long run time. Objective: To investigate different importance analysis techniques with an aim to find alternative technique with shorter run time that will identify parameters with greatest contribution to uncertainty in model output. Methods: A health economics model was updated and served as a tool to implement various importance analysis techniques. Twelve alternative techniques were applied: rank correlation analysis, contribution to variance analysis, mutual information analysis, dominance analysis, regression analysis, analysis of elasticity, ANCOVA, maximum separation distances analysis, sequential bifurcation, double MCS EVPPI,EVPPI-quadrature and EVPPI- single method. Results: Among all these techniques, the dominance measure resulted with the closest correlated calibrated scores when compared with EVPPI calibrated scores. Performing a dominance analysis as a screening method to identify subgroup of parameters as candidates for being most important parameters and subsequently only performing EVPPI analysis on the selected parameters will reduce the overall run time.

Turnover is one of the most important phenomena for management scholars and practitioners. Yet, researchers and practitioners are often frustrated by their inability to accurately predict why individuals leave their jobs. This should be worrisome given that total replacement costs can exceed 100% of an employee’s salary (Cascio, 2006) and can represent up to 40% of a firm’s pre-tax income (Allen, 2008). Motivated by these concerns, the purpose of this study was to assess the predictive validity of commonly-investigated correlates and, by extension, conceptualizations of employee turnover using a large-scale database of scientific findings. Results indicate that job satisfaction, organizational commitment, and embeddedness (e.g., person-job fit, person-organization fit) may be the most valid proximal predictors of turnover intention. Results for a tripartite analysis of the potential empirical redundancy between job satisfaction and organizational commitment when predicting turnover intention align well with previous research on this topic and generally suggest that the two constructs may be empirically indistinguishable in the turnover context. Taken together, this study has important implications for the turnover and sensitivity analysis literatures. With regard to the sensitivity analysis literature, this study demonstrates the application of a sensitivity analysis for relative importance weights in the meta-analytic context. This new method takes into account variance around the meta-analytic mean effect size estimate when imputing relative importance weights and may be adapted to other correlation matrix-based techniques (i.e., structural equation modeling) that are often used to test theory.

Neapibrėžtumo analizė techninių sistemų modeliavimo rezultatams yra ypač aktuali, kai modeliuojamas pavojingų sistemų darbas, saugą užtikrinančių sistemų funkcionavimas, nagrinėjami avarijų scenarijai ar kiti, su rizika susiję klausimai. Tokiais atvejais, ypatingai reaktorių saugos analizės srityje, yra labai svarbu, kad gauti modeliavimo rezultatais būtų robastiški. Šiame darbe yra atliekama Ignalinos AE tikimybinio saugos vertinimo modelio neapibrėžtumo ir jautrumo analizė. Neapibrėžtumo ir jautrumo analizė atlikta naudojantis skirtingais statistinio vertinimo metodais, taikant programų paketą SUSA. Gauti rezultatai palyginti su tikimybinio modeliavimo sistemos Risk Spectrum PSA tyrimo rezultatais. Palyginimas parodė, jog skirtingais metodais ir programiniais paketais parametrų reikšmingumas įvertintas vienodai. Statistinė neapibrėžtumo ir jautrumo analizė, taikant Monte Karlo modeliavimo metodą, leido nustatyti parametrus turėjusius didžiausią įtaką modelio rezultatui. / The uncertainty estimation is the part of full analysis for modelling of safety system functioning in case of the accident, for risk estimation and for making the risk-based decision. In this paper the uncertainty and sensitivity analysis of Ignalina NPP probabilistic safety assessment model was performed using SUSA software package. The results were compared with the results, performed using software package Risk Spectrumm PSA. Statistical analysis of uncertainty and sensitivity allows to estimate the influence of parameters on the calculation results and find those modelling parameters that have the largest impact on the result. Conclusions about for importance of a parameters and sensitivity of the result are obtained using a linear approximation of the model under analysis.